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Field studies conducted over time to collect any type of plant response to a set of treatments are often not treated as repeated measures data. The most used approaches for statistical analyses of this type of longitudinal data are based on separate analyses such as ANOVA, regression, or time contrasts. In many instances, during the review of manuscripts, reviewers have asked researchers to treat year, for example, as a random effect and ignore the interactions between year and other main effects. One drawback of this approach is that the correlation between measurements taken on the same subject over time is ignored. Here, we show that avoiding the covariance between measurements can induce erroneous (e.g., no differences reported when they exist, or differences reported when they actually do not exist) inference of treatment effects. Another issue that has received little attention for statistical inference of multi-year field experiments is the combination of fixed, random, and repeated measurement effects in the same statistical model. This type of analysis requires a more in-depth understanding of modeling error terms and how the statistical software used translates the statistical language of the given command into mathematical computations. Ignoring possible significant interactions among repeated, fixed, and random effects might lead to an erroneous interpretation of the data set. In this manuscript, we use data from two field experiments that were repeated during two and three consecutive years on the same plots to illustrate different modeling strategies and graphical tools with an emphasis on the use of mixed modeling techniques with repeated measures. [ABSTRACT FROM AUTHOR]

Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R2 = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R2 = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models. [ABSTRACT FROM AUTHOR]

Potassium (K) is a primary macronutrient for overall plant growth, yield potential, product quality and stress resistance of crops. Potato (Solanum tuberosum L.) crops require a high amount of potassium to achieve the ideal yield and quality. Therefore, the determination of optimum K rate and efficient source for potato is necessary because K affects crop physiological processes, dry matter production, cooking, and processing requirements. Through modeling on the pooled data extracted from 62 studies, the highest tuber yields might be obtained at an exchangeable soil K level of 200 mg kg−1 approximately, dependent on soil pH, texture, and organic matter. Through modeling on the data of 48 studies, it also revealed that application of potassium sulfate (K2SO4) and potassium chloride (KCl) at rates of 200 kg ha−1 and potassium nitrate (KNO3) at a rate of 100 kg ha−1 might achieve the ideal yield, implying the importance of K sources in potato production. However, these values (either soil exchangeable K content, or fertilizer rates) might not be applicable in a specific growing environment for a specific potato variety. It seems that there is no discrimination among split, pre-plant or in-season application of K, although pre-plant fertilization might be a trustworthy strategy for economic tuber yield. Owing to the luxury consumption of K by potato crop, a combination of factors, including soil exchangeable K level, petiole K concentration, crop removal amount, soil conditions, management practices, climatic conditions, and potato variety, should be considered in order to make rational K fertilizer recommendations. [ABSTRACT FROM AUTHOR]

Commercial sweet corn (Zea mays convar. saccharata var. rugosa) production has a proportionally high potential for nutrient loss to waterways, due to its high nitrogen (N) requirements and low N use efficiency. Cover crops planted after sweet corn can help ameliorate N lost from the field, but farmers are reluctant to utilize cover crops due to a lack of economic incentive. Pennycress (Thlaspi arvense L.) is a winter annual that can provide both economic and environmental benefits. Five N-rates (0, 65, 135, 135 split and 200) were applied pre-plant to sweet corn. After the sweet corn harvest, pennycress was planted into the sweet corn residue with two seeding methods and harvested for seed the following spring. Residual inorganic soil N (Nmin), pennycress biomass, biomass N and yield were measured. The nitrogen rate and seeding method had no effect on pennycress yield, biomass, or biomass N content. The nitrogen rate positively affected Nmin at pennycress seeding, wherein 200N plots had 38–80% higher Nmin than 0N plots, but had no effect on Nmin at pennycress harvest. Control treatments without pennycress had an average of 27–42% greater Nmin. In conclusion, pennycress can act as an effective N catch crop, and produce an adequate seed yield after sweet corn without the need for supplemental fertilization. [ABSTRACT FROM AUTHOR]

Recently released potato cultivars Dakota Russet and Easton were bred for low reducing sugars, and low acrylamide-forming potential in French fries. The objectives of this study were to determine: (1) the effects of nitrogen rate and storage time on tuber glucose concentrations in different cultivars; (2) the relationships between acrylamide, glucose, and asparagine for the new cultivars and Russet Burbank. The study was conducted at Becker, Minnesota over a period of two years on a loamy sand soil under irrigated conditions. All cultivars were subjected to five N rates from 135 to 404 kg ha−1 in a randomized complete block design. Following harvest, tubers were stored at 7.8 °C and sampled at 0, 16, and 32 weeks. Dakota Russet and Easton had significantly lower concentrations of stem- and bud-end glucose, asparagine, and acrylamide than those of Russet Burbank in both years. The effect of storage time on glucose concentration was significant but differed with cultivar and year. N rate effects on stem- and bud-end glucose concentrations were cultivar and storage time dependent. After 16 weeks of storage, both asparagine and acrylamide concentrations linearly increased with increasing N rate. Glucose concentration was positively correlated with acrylamide concentration (r2 = 0.61). Asparagine concentration was also positively correlated with acrylamide concentration (r2 = 0.45) when the asparagine:glucose ratio was <1.306. The correlation between fry color and stem-end glucose concentration was significant over three cultivars in both years, but stronger in a growing season with minimal environmental stress. Taken together, these results suggest that while acrylamide formation during potato processing is a complex process affected by agronomic practices, environmental conditions during the growing season, and storage conditions, cultivar selection may be the most reliable method to minimize acrylamide in fried products. [ABSTRACT FROM AUTHOR]